Comparative Analysis of Data Modeling Design Tools
Conceptual modeling describes the physical or social aspects of the world abstractly, encompassing the interpretation of data production, gathering, visualization, and analysis. The quality of the data analysis system will limit the excellence of any decision-making process. Thus, accurately specify...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9664577/ |
| _version_ | 1818753698285748224 |
|---|---|
| author | Goncalo Carvalho Sergii Mykolyshyn Bruno Cabral Jorge Bernardino Vasco Pereira |
| author_facet | Goncalo Carvalho Sergii Mykolyshyn Bruno Cabral Jorge Bernardino Vasco Pereira |
| author_sort | Goncalo Carvalho |
| collection | DOAJ |
| description | Conceptual modeling describes the physical or social aspects of the world abstractly, encompassing the interpretation of data production, gathering, visualization, and analysis. The quality of the data analysis system will limit the excellence of any decision-making process. Thus, accurately specifying the physical data model is essential. The primary goal of our work is to compare tools that can create this physical model. We recognize several types of data models, but we only include the relational data model. We evaluate free and commercial data modeling tools. But it is challenging to decide how to compare them and which elements are crucial. We propose a new approach for software tools’ evaluation based on the Business Readiness Rating (BRR) model and the OSSpal evaluation methodology. In this work, we show that this new methodology can be tailored to the needs of each individual developer or team, thus providing proper and meaningful results. Also, by applying this hybrid approach to the evaluation of data modelling tools, we show it can robustly handle the bias from lesser relevant evaluation categories. |
| first_indexed | 2024-12-18T05:11:29Z |
| format | Article |
| id | doaj.art-694c67dbb59b4423865cb27f655131bb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T05:11:29Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-694c67dbb59b4423865cb27f655131bb2022-12-21T21:19:53ZengIEEEIEEE Access2169-35362022-01-01103351336510.1109/ACCESS.2021.31390719664577Comparative Analysis of Data Modeling Design ToolsGoncalo Carvalho0https://orcid.org/0000-0001-7095-5003Sergii Mykolyshyn1https://orcid.org/0000-0003-0851-8165Bruno Cabral2https://orcid.org/0000-0001-9699-1133Jorge Bernardino3https://orcid.org/0000-0001-9660-2011Vasco Pereira4https://orcid.org/0000-0002-4225-9075Department of Informatics Engineering, Centre for Informatics and Systems, University of Coimbra, Coimbra, PortugalDepartment of Informatics Engineering, Centre for Informatics and Systems, University of Coimbra, Coimbra, PortugalDepartment of Informatics Engineering, Centre for Informatics and Systems, University of Coimbra, Coimbra, PortugalDepartment of Informatics Engineering, Centre for Informatics and Systems, University of Coimbra, Coimbra, PortugalDepartment of Informatics Engineering, Centre for Informatics and Systems, University of Coimbra, Coimbra, PortugalConceptual modeling describes the physical or social aspects of the world abstractly, encompassing the interpretation of data production, gathering, visualization, and analysis. The quality of the data analysis system will limit the excellence of any decision-making process. Thus, accurately specifying the physical data model is essential. The primary goal of our work is to compare tools that can create this physical model. We recognize several types of data models, but we only include the relational data model. We evaluate free and commercial data modeling tools. But it is challenging to decide how to compare them and which elements are crucial. We propose a new approach for software tools’ evaluation based on the Business Readiness Rating (BRR) model and the OSSpal evaluation methodology. In this work, we show that this new methodology can be tailored to the needs of each individual developer or team, thus providing proper and meaningful results. Also, by applying this hybrid approach to the evaluation of data modelling tools, we show it can robustly handle the bias from lesser relevant evaluation categories.https://ieeexplore.ieee.org/document/9664577/Data modelingdesign toolsdatabase management systemsdata modeling tools |
| spellingShingle | Goncalo Carvalho Sergii Mykolyshyn Bruno Cabral Jorge Bernardino Vasco Pereira Comparative Analysis of Data Modeling Design Tools IEEE Access Data modeling design tools database management systems data modeling tools |
| title | Comparative Analysis of Data Modeling Design Tools |
| title_full | Comparative Analysis of Data Modeling Design Tools |
| title_fullStr | Comparative Analysis of Data Modeling Design Tools |
| title_full_unstemmed | Comparative Analysis of Data Modeling Design Tools |
| title_short | Comparative Analysis of Data Modeling Design Tools |
| title_sort | comparative analysis of data modeling design tools |
| topic | Data modeling design tools database management systems data modeling tools |
| url | https://ieeexplore.ieee.org/document/9664577/ |
| work_keys_str_mv | AT goncalocarvalho comparativeanalysisofdatamodelingdesigntools AT sergiimykolyshyn comparativeanalysisofdatamodelingdesigntools AT brunocabral comparativeanalysisofdatamodelingdesigntools AT jorgebernardino comparativeanalysisofdatamodelingdesigntools AT vascopereira comparativeanalysisofdatamodelingdesigntools |